{"title":"Reconciling the Personalization-Privacy Paradox: Exploring Privacy Boundaries in Online Personalized Advertising","authors":"Yu-Qian Zhu, Kritsapas Kanjanamekanant, Yi-Te Chiu","doi":"10.17705/1jais.00775","DOIUrl":null,"url":null,"abstract":"To reconcile the personalization-privacy paradox, we adopt the privacy as a state view and define privacy as a state of information boundary rule-following. We further identify five types of boundaries underlying some of the important implicit rules of maintaining privacy: communication channel, platform, device, temporal, and purpose boundaries. Using an online vignette survey, we investigated how each of these boundary types affected users’ privacy perceptions when they were subjected to personalized advertisements. Using fixed- and random-effects models, we investigated how violating different boundary rules leads to changes in perceived privacy. Our results show that all five boundary types are significant predictors of perceived privacy within individuals. The communication channel, device, and business versus private purpose are significant predictors of perceived privacy across the whole sample. Temporal boundaries and platform boundaries failed to achieve statistical significance when evaluated simultaneously with the other factors across the whole sample. This means that for each individual, observing the rules of these five boundary types leads to higher perceived privacy than not observing these conditions. Taken as a whole, observing communication channel, device, and business versus private purpose boundaries also leads to higher averages of perceived privacy across the whole sample. Theoretical and practical implications are discussed based on the results","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"25 1","pages":"1"},"PeriodicalIF":7.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Association for Information Systems","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.17705/1jais.00775","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1
Abstract
To reconcile the personalization-privacy paradox, we adopt the privacy as a state view and define privacy as a state of information boundary rule-following. We further identify five types of boundaries underlying some of the important implicit rules of maintaining privacy: communication channel, platform, device, temporal, and purpose boundaries. Using an online vignette survey, we investigated how each of these boundary types affected users’ privacy perceptions when they were subjected to personalized advertisements. Using fixed- and random-effects models, we investigated how violating different boundary rules leads to changes in perceived privacy. Our results show that all five boundary types are significant predictors of perceived privacy within individuals. The communication channel, device, and business versus private purpose are significant predictors of perceived privacy across the whole sample. Temporal boundaries and platform boundaries failed to achieve statistical significance when evaluated simultaneously with the other factors across the whole sample. This means that for each individual, observing the rules of these five boundary types leads to higher perceived privacy than not observing these conditions. Taken as a whole, observing communication channel, device, and business versus private purpose boundaries also leads to higher averages of perceived privacy across the whole sample. Theoretical and practical implications are discussed based on the results
期刊介绍:
The Journal of the Association for Information Systems (JAIS), the flagship journal of the Association for Information Systems, publishes the highest quality scholarship in the field of information systems. It is inclusive in topics, level and unit of analysis, theory, method and philosophical and research approach, reflecting all aspects of Information Systems globally. The Journal promotes innovative, interesting and rigorously developed conceptual and empirical contributions and encourages theory based multi- or inter-disciplinary research.